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Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correlation with pathology and related therapeutics, including dialysis and renal transplantation. Rapid advances in the omics sciences, medical device sensorization, and networked digital medical devices have made such research increasingly data centered. Data-centric science requires the support of computationally powerful and sophisticated tools able to handle the overflow of novel biomarkers and therapeutic targets. This is a context in which artificial intelligence (AI) and, more specifically, machine learning (ML) can provide a clear analytical advantage, given the rapid advances in their ability to harness multimodal data, from genomic information to signal, image and even heterogeneous electronic health records (EHR). However, paradoxically, only a small fraction of ML-based medical decision support systems undergo validation and demonstrate clinical usefulness. To effectively translate all this new knowledge into clinical practice, the development of clinically compliant support systems based on interpretable and explainable ML-based methods and clear analytical strategies for personalized medicine are imperative. Intelligent nephrology, that is, the design and development of AI-based strategies for a data-centric approach to nephrology, is just taking its first steps and is by no means yet close to its coming of age. These first steps are not even homogeneously taken, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities. With all this in mind, this editorial aim to provide a selective overview of the current use of AI technologies in nephrology and heralds the "Artificial Intelligence in Nephrology" special issue launched by BMC Nephrology., (© 2024. The Author(s).)

Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world's first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R 2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance.

Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part's integrity or causing damage to the component being tested. This includes monitoring materials' condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.

Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure's key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.

To address the problems of low accuracy in fault diagnosis of oil-immersed transformers, poor state perception ability and real-time collaboration during diagnosis feedback, a fault diagnosis method for transformers based on the integration of digital twins is proposed. Firstly, fault sample balance is achieved through Iterative Nearest Neighbor Oversampling (INNOS), Secondly, nine-dimensional ratio features are extracted, and the correlation between dissolved gases in oil and fault types is established. Then, sparse principal component analysis (SPCA) is used for feature fusion and dimensionality reduction. Finally, the Aquila Optimizer (AO) is introduced to optimize the parameters of the Kernel Extreme Learning Machine (KELM), establishing the optimal AO-KELM diagnosis model. The final fault diagnosis accuracy reaches 98.1013%. Combining transformer digital twin models, real-time interaction mapping between physical entities and virtual space is achieved, enabling online diagnosis of transformer faults. Experimental results show that the method proposed in this paper has high diagnostic accuracy and strong stability, providing reference for the intelligent operation and maintenance of transformers. [ABSTRACT FROM AUTHOR]

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU. [ABSTRACT FROM AUTHOR]

While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network (NN) is proposed here to bypass this difficulty within the DT. Further, an algorithm inspired from machine learning is employed to solve the underlying semi-Markov decision process, whose transition model is captured via the NN. Numerical studies are carried out to demonstrate the usefulness of the approach. Finally, convergence properties of the algorithm are analyzed mathematically. [ABSTRACT FROM AUTHOR]

Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO2 per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. [ABSTRACT FROM AUTHOR]

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications. [ABSTRACT FROM AUTHOR]

This review focuses on the definitions, modalities, applications, and performance of various aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this regard, the context around Industry 4.0 and even aspirations for Industry 5.0 are discussed. The many definitions and interpretations of DTs in this domain are first summarized. Subsequently, their adoption and performance levels for rotating and industrial machineries for manufacturing and lifetime performance are observed, along with the type of validations that are available. A significant focus on integrating fundamental operations of the system and scenarios over the lifetime, with sensors and advanced machine or deep learning, along with other statistical or data-driven methods are highlighted. This review summarizes how individual aspects around DTs are extremely helpful for lifetime design, manufacturing, or decision making even when a DT can remain incomplete or limited. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

Formation testing is widely used in offshore oil and gas development, and predicting the sampling time of pure fluids during this process is very important. However, existing formation testing methods have problems such as long duration and low efficiency. To address these issues, this paper proposes a hybrid-driven method based on physical models and machine learning models to predict fluid sampling time in formation testing. In this hybrid-driven model, we establish a digital twin model to simulate a large amount of experimental data (6000 cases, totaling over 1 million data points) and significantly enhance the correlation between features using physical formulas. By applying advanced machine learning algorithms, we achieve real-time predictions of fluid sampling time with an accuracy of up to 92%. Additionally, we use optimizers to improve the model's accuracy by 3%, ultimately reaching 95%. This model provides a novel approach for optimizing formation testing that is significant for the efficient development of offshore oil and gas. [ABSTRACT FROM AUTHOR]

The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupons, wirelessly transmitting these data to a central server for processing. A deep neural network (DNN) model predicts mechanical properties and can be extended to forecast structural failures, facilitating proactive maintenance and enhancing safety. The modular design supports scalability and can be embedded within digital twin frameworks, offering significant benefits to aircraft operators and manufacturers. The system utilizes an ML model with a mean absolute error (MAE) of 0.14 on test data for forecasting mechanical properties. Data transmission latency throughout the entire system is less than one second in a LAN setup, highlighting its potential for realtime monitoring applications in aerospace and other industries. However, while the system shows promise, challenges such as sensor reliability under extreme environmental conditions and the need for advanced ML models to handle diverse data streams have been identified as areas for future research. [ABSTRACT FROM AUTHOR]

Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge. [ABSTRACT FROM AUTHOR]

Simple Summary: We utilized publicly available perturbation phosphoproteomic data to construct models elucidating cell state transitions across multiple breast cancer and normal breast tissue-derived cell lines. Employing a hybrid methodology, which integrates machine learning and mechanistic modeling, we separated luminal, basal, and normal cell states and revealed core networks that control cell state transitions. We determined causal connections within the core networks and developed interpretable mechanistic models that elucidated the drivers of cell phenotypes. Significantly, these models can predict synergistic drug combinations capable of potentially reversing oncogenic transformation in breast cancer cell lines. Our methodology will enable designer approaches to identify targeted perturbations that convert cell states and mechanistically underpin therapeutic interventions. Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors. [ABSTRACT FROM AUTHOR]

Digital twin technology significantly enhances construction site management efficiency; however, dynamically reconstructing site activities presents a considerable challenge. This study introduces a methodology that leverages camera data for the 3D reconstruction of construction site activities. The methodology was initiated using 3D scanning to meticulously reconstruct the construction scene and dynamic elements, forming a model base. It further integrates deep learning algorithms to precisely identify static and dynamic elements in obstructed environments. An enhanced semi-global block-matching algorithm was then applied to derive depth information from the imagery, facilitating accurate element localization. Finally, a near-real-time projection method was introduced that utilizes the spatial relationships among elements to dynamically incorporate models into a 3D base, enabling a multi-perspective view of site activities. Validated by simulated construction site experiments, this methodology showcased an impressive reconstruction accuracy reaching up to 95%, this underscores its significant potential in enhancing the efficiency of creating a dynamic digital twin model. [ABSTRACT FROM AUTHOR]

In a construction project schedule, delays in delivery are one of the most important problems. Delays can be caused by several project components; however, the issue is amplified when delays occur simultaneously. Classifying delays is relevant in order to allocate responsibility to the parties. In Italy, the delay in the delivery of medium and large-sized works in residential urban centers is about 15% compared to the project forecast. Moreover, the AECO sector's ability to adapt to emerging challenges, such as environmental sustainability and digitization, is limited by the lack of innovation in management methods. The aim of this research is to create a methodology for managing the built and to-be-built environment in a digital way. This will optimize the building process by reducing delays and waste of resources. The methodology will use tools such as digital twin (DT), Building Information Modeling (BIM), Internet of Things (IoT), and Artificial Intelligence (AI) algorithms. The integration of lean construction practices can make the use of these technologies even more efficient, supporting better workflow management by using the BIM environment. The paper presents a methodology that can be applied to various scaling factors and scenarios. It is also useful for construction sites that are already in progress. As highlighted below, this brings significant economic-temporal advantages. [ABSTRACT FROM AUTHOR]

The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient's digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application's response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application's potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals. [ABSTRACT FROM AUTHOR]

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022–2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine. [ABSTRACT FROM AUTHOR]

Digital twins represent a promising approach for sustainable building operations and management in the context of the carbon neutrality goals of the European Union (EU). Using OpenStudio, an opensource platform for building energy modeling, we demonstrated the creation and editing of building digital twins. OpenStudio provides a user-friendly interface and extensive simulation capabilities, allowing detailed and accurate modeling of building components and systems. Using OpenStudio Measures, users can automate tasks and customize simulation models to optimize the building performance. The process of creating a building digital twin involves collecting historical data and accurately representing the building geometry; materials; schedules; and heating, ventilation, and air conditioning (HVAC) systems. Challenges such as data availability and model accuracy highlight the importance of modeling practices. Editing the digital twin involves modifying the OpenStudio model files and EnergyPlus weather files to simulate different building operation scenarios. Python programming language opportunities were considered for digital twin file modification. The potential of digital twins lies in their ability to simulate future building conditions and optimize building system settings. By integrating digital twins with machine learning algorithms and connecting them directly to building management systems, optimal building control strategies can be automated, thereby reducing energy consumption and improving occupant comfort levels. [ABSTRACT FROM AUTHOR]

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery. [ABSTRACT FROM AUTHOR]

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory. [ABSTRACT FROM AUTHOR]

Machine learning deals with creating algorithms capable of learning from the provided data. These systems have a wide range of applications and can also be a valuable tool for scientific research, which in recent years has been focused on finding new diagnostic techniques for particle accelerator beams. In this context, SPARC_LAB is a facility located at the Frascati National Laboratories of INFN, where the progress of beam diagnostics is one of the main developments of the entire project. With this in mind, we aim to present the design of two neural networks aimed at predicting the spot size of the electron beam of the plasma-based accelerator at SPARC_LAB, which powers an undulator for the generation of an X-ray free electron laser (XFEL). Data-driven algorithms use two different data preprocessing techniques, namely an autoencoder neural network and PCA. With both approaches, the predicted measurements can be obtained with an acceptable margin of error and most importantly without activating the accelerator, thus saving time, even compared to a simulator that can produce the same result but much more slowly. The goal is to lay the groundwork for creating a digital twin of linac and conducting virtualized diagnostics using an innovative approach. [ABSTRACT FROM AUTHOR]

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry. [ABSTRACT FROM AUTHOR]

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods. [ABSTRACT FROM AUTHOR]

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs. [ABSTRACT FROM AUTHOR]

Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated cyber risk assessment framework is essential for proactive risk mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in the preliminary stages. This study introduces a cyber risk assessment framework that integrates machine learning techniques, pioneering a data-driven approach to quantitatively assess cyber risks while considering industry-specific vulnerabilities. The framework builds on over 20 literature reviews related to construction cybersecurity and semi-structured interviews with two industry experts, ensuring both rigor and alignment with practical industrial needs. This study also addresses the challenges of data collection and proposes potential solutions, such as a standardized data collection format with preset fields that computers can automatically populate using data from construction companies. Additionally, the framework proposes dynamic machine learning models that adjust based on new data, facilitating continuous risk monitoring tailored to industry needs. Furthermore, this study explores the potential of advanced language models in cybersecurity management, positioning them as intelligent cybersecurity consultants that provide answers to security inquiries. Overall, this study develops a conceptual machine learning framework aimed at creating a robust, off-the-shelf cyber risk management system for industry practitioners. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) offers new possibilities in soft robotics as materials can easily be combined in multi-material designs. Proper sensing is essential for the soft actuators to interact with the surroundings successfully. By fabricating sensors through AM, sensors can be embedded directly into the components during manufacturing. This paper investigates NinjaTek Eels electrical resistance response to strain and the feasibility of using the material to create strain sensors. Strain sensors were 3D-printed out of NinjaTek Eel, a soft conductive TPU, and was tested during cyclic loading. A custom resistance–strain test rig was developed for measuring sensor behavior. The rig was calibrated for electric resistance, able to measure electric resistance as a function of strain. A parabolic response curve was observed during cyclic loading, which led to ambiguous readings. A 10-specimen validation test was conducted, evaluating the statistical variation for the first 100 loading cycles. The validation test showed that the sensor is capable of accurate and predictable readings during single load cases and cyclic loading, with the overall root mean square error being 66.9 Ω. Combining two sensors of different cross-sections gave promising results in terms of calibrating. By monitoring load cycles and strain rates, calibration can also be achieved by machine learning models by the microcontroller used to extract data. The presented work in this article explores the potential of using conductive TPUs as sensors embedded in products such as soft robotics, life monitoring of products with structural, and digital twins for live product to user feedback. [ABSTRACT FROM AUTHOR]

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the Bald Eagle Search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization --ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers. [ABSTRACT FROM AUTHOR]

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations. [ABSTRACT FROM AUTHOR]

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System. [ABSTRACT FROM AUTHOR]

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Numerical modeling tools are essential in aircraft structural design, yet they face challenges in accurately reflecting real-world behavior due to factors like material properties scatter and manufacturing-induced deviations. This article addresses the potential impact of digital twins on overcoming these limitations and enhancing model reliability through advanced updating techniques based on machine learning. Digital twins, which are virtual replicas of physical systems, offer a promising solution by integrating sensor data, operational inputs, and historical records. Machine learning techniques enable the calibration and validation of models, combining experimental inputs with simulations through continuous updating processes that refine digital twins, improving their accuracy in predicting structural behavior and performance throughout an aircraft's life cycle. These refined models enable real-time monitoring and precise damage assessment, supporting decision making in diverse contexts. By integrating sensor data and updating techniques, digital twins contribute to improved design and maintenance operations by providing valuable insights into structural health, safety, and reliability. Ultimately, this approach leads to more efficient and safer aviation operations, demonstrating the potential of digital twins to revolutionize aircraft structural analysis and design. This article explores various advancements and methodologies applicable to structural assessment, leveraging machine learning tools. These include the utilization of physics-informed neural networks, which enable the handling of diverse uncertainties. Such approaches empower a more informed and adaptive strategy, contributing to the assurance of structural integrity and safety in aircraft structures throughout their operational life. [ABSTRACT FROM AUTHOR]

The current outdoor thermal comfort index assessment is either based on thermal sensation votes collected through field surveys/questionnaires or using equations fundamentally backed by thermodynamics, such as the widely used UTCI and PET indices. The predictive ability of all methods suffers from discrepancies as multi-sensory attributes, cultural, emotional, and psychological cognition factors are ignored. These factors are proven to influence the thermal sensation and duration people spend outdoors, and are equally prominent factors as air temperature, solar radiation, and relative humidity. The studies that adopted machine learning models, such as Artificial Neural Networks (ANNs), concentrated on improving the predictive capability of PET, thereby making the field of Artificial Intelligence (AI) domain underexplored. Furthermore, universally adopted outdoor thermal comfort indices under-predict a neutral thermal range, for a reason that is linked to the fact that all indices were validated on European/American subjects living in temperate, cold regions. The review highlighted gaps and challenges in outdoor thermal comfort prediction accuracy by comparing traditional methods and Industry 4.0. Additionally, a further recommendation to improve prediction accuracy by exploiting Industry 4.0 (machine learning, artificial reality, brain–computer interface, geo-spatial digital twin) is examined through Knowledge Translation. [ABSTRACT FROM AUTHOR]

Background: Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology: Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results: The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions: Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors. [ABSTRACT FROM AUTHOR]

Roads as vital public assets are the backbone for transportation systems and support constant societal development. Recently, data-driven technologies such as digital twins and especially machine learning have shown great potential to maintain the service level of the existing road infrastructure by accurate future condition modelling and optimal maintenance treatment recommendations. However, the pavement community suffers from inadequate data and errors experienced in data collection, which unavoidably limits machine learning performance. In addition, focusing solely on data without considering the underlying physical behaviour remains as a challenge for the practical implementation of machine learning. To this end, this study provides a machine learning based approach to predict road rutting taking into account the machine learning uncertainties. The US Long-Term Pavement Performance public database has been used as the main data source while supplementary synthetic data was added using Finite Element simulations based on physics. The obtained results indicate that adding extra simulation data improved the model’s short-term prediction accuracy by 4.4% and reduced the long-term prediction uncertainty by 6.76%. The approach could potentially mitigate the issue of lack of data and the uncertainties around the data collected, by integrating existing understanding of pavement physical behaviour into the machine learning modelling pipeline.

This document discusses the integration of machine learning (ML), Internet of Things (IoT), and digital twin (DT) technologies and their transformative potential across various fields. The authors highlight the challenges posed by the vast amounts of data generated by IoT and the need for advanced tools like ML to process and extract meaningful insights from this data. They also explore the concept of digital twins, which create virtual replicas of physical entities and enable real-time simulation and optimization. The document includes a collection of research papers that showcase the innovative applications of these technologies in areas such as maintenance scheduling, healthcare services, structural integrity, safety management, security, traffic control, and physical activity coaching. The authors hope that readers will find inspiration and insights from these studies to drive further innovation in this dynamic field. [Extracted from the article]

The article explores the application of digital twins in neuroscience, emphasizing their potential for modeling complex diseases like multiple sclerosis and predicting disease progression. It also discusses the use of digital twins for personalized patient care, the benefits and challenges of integrating such technology in healthcare, and the need for standardization and expert consensus in developing digital twin models.

Abstract Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.

Advancements in battery management systems (BMS) involve using digital twins to optimize battery performance in electric vehicles. The state of charge and health estimations are essential for battery efficiency and longevity. Digital twins allow for precise predictions of the state of charge and state of health by simulating battery behavior under different conditions. Using artificial intelligence (AI) in digital twins improves predictive capabilities, as demonstrated through studies employing deep neural networks (DNN) and long short-term memory networks (LSTM). However, incorporating AI presents challenges due to the opaque nature of the models, necessitating the need for explainable artificial intelligence (XAI) and trustworthy digital twin models. This study pioneered XAI methods such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and linear regression-based surrogate models to explain the predictions of DNNs and LSTMs in digital twin-supported BMSs. The results reveal that the DNN and LSTM digital twin models are more reliable for state-of-health and state-of-charge estimation due to higher $R^{2}$ scores, lower mean residuals, and better XAI results.

Current research in air-conditioning and mechanical ventilation (ACMV) operation focuses on isolated sub-processes and analytical models. Digital twins, as digital replicas of assets, processes, or systems in the built environment, enable facilities manager (FM) to gain insights into the physical features of space, equipment performance, and energy efficiency. This study presents the 3D reconstruction of semantic-rich digital twins, which encompasses conditional and machine learning-enabled monitoring with 3D geometric models, for ACMV modeling and operation. The proposed framework involves a hybrid rule-based and data-driven approach to forecast the performance of indoor environment and identify potential anomalies throughout ACMV operation. Following this, a scan-to-BIM process is undertaken, with the aid of Simultaneous Localization and Mapping algorithms, to semi-automatically generate the as-built geometric models. Lastly, semantic enrichment of BIM is performed by incorporating time-series data from the rule-based and data-driven approach with 3D geometric models. The proposed approach supports the reconstruction of content-aware and semantic-rich digital twins, which utilize sensor-derived time-series data and 3D geometric models, to conduct advanced analysis for intelligent ACMV operation towards energy efficiency and occupant comfort.

Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework. [ABSTRACT FROM AUTHOR]

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. [ABSTRACT FROM AUTHOR]

The development of novel and robust digital methods to support the maintenance of existing road infrastructure requires a large amount of harmonized data. Especially in the context of automated modelling having a large amount of matching data from different perspectives enables disruptive, new use cases that might largely impact the efficiency in maintenance of the built environment. Unfortunately, such data compositions are tedious to collect in real world applications, due to many influential factors, leading to deviations between multiple data sources and the sheer complexity in the process of creating a digital model. However, for deep learning applications, a large amount of carefully annotated data is necessary for robust estimations. In this contribution, we tackle this problem by presenting a novel procedural modelling and model configuration approach for generating homogeneous data combinations to step towards direct parameter estimation for machine learning approaches utilizing point clouds of roads and end-to-end model generation of digital road models. [ABSTRACT FROM AUTHOR]

Digital twins have emerged as integral components of modern industrial operations, owing to their ability to create virtual replicas of physical assets, processes, or systems. This article thoroughly explores the manifold applications of digital twins within the industry, emphasizing their role in enabling data-driven decision-making. These virtual counterparts facilitate real-time monitoring, analysis, and optimization, thereby enhancing operational efficiency. Additionally, they play a crucial role in predictive maintenance by minimizing downtime and prolonging the lifespan of machinery. By expediting testing and simulation procedures, digital twins promote innovation and efficiency gains even before real-world implementation. Moreover, they foster interdisciplinary collaboration and mutual understanding, particularly through collaborative digital twin platforms. These platforms act as catalysts for Industry 4.0, integrating various technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML). As the influence of digital twins continues to grow within industrial processes, they serve as conduits for innovation, driving the trajectory of industrial advancement forward. [ABSTRACT FROM AUTHOR]